Axis-Parallel Dimension Reduction for Biometric Research

Abstract

The objective of this research is to present a novel methodology based on the axis-parallel dimension reduction technique and chaotic neural network to improve the performance and circumvention of multi-modal biometric system. The proposed methodology for dimensionality-reduction and chaotic neural network learner on example of face, ear and fingerprint biometric was presented in the methodology section. This paper validates the proposed methodology by providing experimentation results. First subsection showcases advantages of chaotic neural network for fingerprint recognition (accuracy and circumvention). Next subsection compares results of the proposed multi-biometric system based on axis-parallel dimensionality reduction. The experiments demonstrate that the proposed dimensionality reduction and associative memory training methodology outperforms other commonly used techniques in both FAR (False Accept Rate) and FRR (False Reject Rate) both individually and if used together. Finally, the last section proposes the alternative multimodal system architecture with additional features that can further improve system performance in terms of accuracy and circumvention.